Which of the following is a drawback of using backward selection compared to best subset selection in model selection?

Backward selection is a model selection technique that starts with a full model containing all candidate predictors and systematically removes the least significant variables. One of its drawbacks, particularly when compared to best subset selection, is that it may not fully leverage the special knowledge or insight that a researcher has regarding the variables.

In backward selection, decisions about which variables to remove are primarily based on statistical criteria, such as p-values or AIC (Akaike Information Criterion), rather than incorporating the domain knowledge or theoretical considerations of the researcher. This could result in the omission of important variables that the researcher believes should be included based on their understanding of the subject matter.

Best subset selection, on the other hand, evaluates all possible combinations of predictors and allows for a more nuanced approach to model selection, potentially incorporating the researcher's insights more effectively. This means that while backward selection is a more automated process, it runs the risk of missing crucial predictors simply because they do not meet certain statistical thresholds during the elimination process.

Thus, the reliance on automated statistical criteria in backward selection may overlook the valuable context that the researcher's knowledge provides, which is often critical in creating a robust model that accurately reflects the underlying phenomena being studied.